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- Title
A COMPARISON OF MACHINE-LEARNING REGRESSION ALGORITHMS FOR THE ESTIMATION OF LAI USING LANDSAT - 8 SATELLITE DATA.
- Authors
Yadav, V. P.; Prasad, R.; Bala, R.; Vishwakarma, A. K.; Yadav, S. A.; Singh, S. K.
- Abstract
The leaf area index (LAI) is one of key variable of crops which plays important role in agriculture, ecology and climate change for global circulation models to compute energy and water fluxes. In the recent research era, the machine-learning algorithms have provided accurate computational approaches for the estimation of crops biophysical parameters using remotely sensed data. The three machine-learning algorithms, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) were used to estimate the LAI for crops in the present study. The three different dates of Landsat-8 satellite images were used during January 2017 – March 2017 at different crops growth conditions in Varanasi district, India. The sampling regions were fully covered by major Rabi season crops like wheat, barley and mustard etc. In total pooled data, 60% samples were taken for the training of the algorithms and rest 40% samples were taken as testing and validation of the machinelearning regressions algorithms. The highest sensitivity of normalized difference vegetation index (NDVI) with LAI was found using RFR algorithms (R2 = 0.884, RMSE = 0.404) as compared to SVR (R2 = 0.847, RMSE = 0.478) and ANNR (R2 = 0.829, RMSE = 0.404). Therefore, RFR algorithms can be used for accurate estimation of LAI for crops using satellite data.
- Subjects
VARANASI (Uttar Pradesh, India); NORMALIZED difference vegetation index; GENERAL circulation model; LEAF area index; BARLEY; CLIMATE change; ARTIFICIAL neural networks
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2019, Vol XLII-4/W16, p679
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLII-4-W16-679-2019